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Adaptive
Signal Processing |
Master
level |
No.
of credits: 7 |
|
Textbooks: |
Sayed, A.H., Fundamentals of Adaptive Filtering, Wiley, 2003
Haykin, S., Adaptive
Filter Theory, 4/E, Prentice-Hall, 2001
J.C. Principe et al., Neural
and Adaptive Systems, Wiley, 2000 |
|
| Tutorials: |
Linear adaptive filtering (Chapter 3 from
my DSP book, in Romanian)
Introduction
to Artificial Neural Networks (Chapter 1 from
my ANN book, in Romanian) |
|
|
| General
description |
|
The course focuses
on advanced topics on adaptive filtering. Main themes are related
to linear filtering algorithms in time and frequency domains,
nonlinear adaptive filters implemented as neural networks, neural
architectures and learning algorithms for feedforward and recurrent
networks. Applications include pattern recognition (OCR and face
processing), data transmission channel equalization and analog
decoding, biomedical time series analysis, system identification.
Software support is provided by MATLAB and NeuroSolutions neural
networks simulator. |
| Course outline |
Lecture 1:
More info: 1
Lab 1 
|
General introduction
to adaptive linear filtering
Optimal
filtering problem and Wiener solution. Definition and characterization
of random processes
Classification criteria of adaptive
algorithms. Cost functions.
Applications of adaptive filters
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Lecture 2:
Lab 2
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First order
adaptive algorithms: gradient descent, Newton algorithm. |
Lecture 3:
More info: 1, 2, 3
Lab 3 HW 1 
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LMS algorithm and its variants.
Adaptive
filtering in the frequency domain |
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Least-Squares (LS) algorithm. Recurrent Least-Squares (RLS) algorithm. |
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Lecture 5:
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General introduction
to Artificial Neural Networks (ANN's)
Motivations for studying ANN's
Definition and classification criteria
for ANN's
Applications of ANN's
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Lecture 6:
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Multilayer perceptron
(MLP)
Standard backpropagation training
algorithm
Variants of backpropagation algorithm
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Lecture 7:
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Radial Basis Functions
(RBF) network
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Lecture 8-9:
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Recurrent neural networks
Analog systems: Hopfield network
- theory and applications, Cellular Neural Networks (CNN)
Discrete systems: Hopfield and Elman
networks - theory and applications
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Lecture 10-12:
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Applications of ANN's
Pattern Recognition
Channel equalization
Digital filter design
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